{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Autocorrelation](05.10-Autocorrelation.ipynb) | [Contents](Index.ipynb) | [6. Spatial Data](06.00-Spatial-Data.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.11 不确定的时间间隔\n",
    "\n",
    "很多时候，我们得到了大量的数据集合。我们希望看看这些集合的行为。让我们首先从生成集合开始，然后绘制它们。在这个例子中，首先我们产生正弦行为的信号。然后，我们使用`vstack`来堆栈数据，即使用许多一维数据来制作二维数组。此后，我们在这些数据中混杂一些噪音，使集合看上去有些不同。我们使用`T`属性转置数据进行绘图，否则将在x轴上绘制集合而不是时间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c9f0cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "\n",
    "# generate some data\n",
    "x = 100*np.sin(np.linspace(0,10,100))\n",
    "X = np.vstack([x,x,x,x,x,x,x,x,x,x,x,x,x,x,x,x,x,x,x,x])\n",
    "e = 10*np.random.randn(20,100)\n",
    "\n",
    "X_err = X+e\n",
    "\n",
    "plt.plot(X_err.T, 'k')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('X')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图5.19:各种数据集合</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图5.19显示了这些集合的图形。我们看到所有的集合都表现得很相似。但是我们不能够使用这张图推断出任何更多关于集合的行为。可视化集合的一个更好的方法是使用不同的不确定区间及平均值。我们可以使用`st.scoreatpercentile`函数计算在不同百分位数的不确定性区间。我们计算第10、50和90的百分位数。第50的百分位是中位数。我们绘制是中位数而不是平均值，因为如果数据中有许多离群值，则中位数提供了更好地了解整体的行为。图5.20显示了集合的中位数、第10、第90百分位。使用这个图，我们可以看出，这个集合的分布不尽相同；在峰谷相对较多，在其他地方则较少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cce2f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ll = st.scoreatpercentile(X_err, 10) # 10th percentile\n",
    "ml = st.scoreatpercentile(X_err, 50) # 50th percentile\n",
    "ul = st.scoreatpercentile(X_err, 90) # 90th percentile\n",
    "\"\"\"\n",
    "ll = []\n",
    "ml = []\n",
    "ul = []\n",
    "\n",
    "for i in range(len(X_err)):\n",
    "    ll.append(st.scoreatpercentile(X_err[i], 10))\n",
    "    ml.append(st.scoreatpercentile(X_err[i], 50))\n",
    "    ul.append(st.scoreatpercentile(X_err[i], 90))\n",
    "\"\"\"    \n",
    "plt.plot(ml, 'g', lw=2, label='Median')\n",
    "plt.plot(ul, '--m', label='90%')\n",
    "plt.plot(ll, '--b', label='10%')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('X')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图5.20：由集合估计的中位数和不确定性区间</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不确定性区间可以由阴影区域绘制出。`plt.fill_between`提供了在两个数组之间填充颜色的选项，并可用于绘制阴影区域。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cce27cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ml, 'g', lw=2, label='Median')\n",
    "plt.fill_between(range(100), ul, ll, color='k', alpha=0.4, label='90%')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('X')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图5.21:由集合估计的中位数和不确定性区间。不确定性区间使用阴影区域显示。</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图5.21显示了使用阴影区域的不确定性图。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
